Led the development of a scalable IoT system managing over 1000 Linux-based edge nodes, achieving 99% uptime and eliminating single points of failure. Developed IoT software in Python to run on edge controllers, collecting data from up to 30 MODBUS RTU devices and BACnet IP protocols, ensuring seamless data collection. Engineered software to process and transmit sensor data between edge nodes and the cloud over MQTT, improving data transfer efficiency by 40%. Implemented a health monitoring system for IoT controllers using InfluxDB and Grafana, tracking 15+ metrics (CPU, memory, network latency, disk I/O), reducing downtime by 25%. Designed an end-to-end process for controller lifecycle management, from testing and provisioning to deployment, increasing deployment speed by 30%. Built an OTA pipeline using AWS IoT Jobs, enabling remote software updates for controllers, cutting manual intervention by 50%. Defined and assessed system reliability metrics such as data quality and system uptime, integrating them with the monitoring system to boost overall system reliability by 20%. Developed a custom logic processing engine, allowing users to automate site processes through flow-based logic, enhancing system flexibility, alerting, and user control. Standardized command request processing from the cloud to control assets, tracking each command across 5 stages with 20 status codes to precisely trace command journeys. Enforced clean code principles and design patterns, improving software reusability, readability, and extensibility. Standardized logging practices and integrated alerting tools to notify developers of controller errors, reducing incident response time by 40%. Spearheaded the development of a cross-platform web app (Ionic, React Native, Flask) to monitor sensor data, perform Modbus scans, and control devices. Built an intelligent tech support assistant chatbot using OpenAI API, RAG, Langchain, and Streamlit, automating 70% of issue diagnosis and resolution, significantly reducing support time.